[100.03] Dynamic Modeling of time series using Artificial Neural Networks

Artificial Neural Networks (ANN) have the ability to adapt to and learn
complex topologies, they represent new technology with which to
explore dynamical systems. Multi-step prediction is used to capture the
dynamics of the system that produced the time series. Multi-step
prediction is implemented by a recurrent ANN trained with trajectory
learning. Two separate memories are employed in training the ANN, the
common tapped delay-line memory and the new gamma memory. This
methodology has been applied to the time series of a white dwarf and
to the quasar 3C 345.